Abstract: A precharge method for a data driver includes steps of: outputting a display data to a plurality of output terminals of the data driver; outputting a second precharge voltage to an output terminal among the plurality of output terminals prior to outputting the display data to the output terminal, to precharge the output terminal to a voltage level closer to an output voltage; and outputting a first precharge voltage to the output terminal prior to outputting the second precharge voltage. The first precharge voltage provides a faster voltage transition on the output terminal than the second precharge voltage.

Abstract: A mechanism is provided for controlling the internal air-quality of a vehicle, including configuring a control policy that controls an internal air-quality of a vehicle and performing an action dictated by the control policy according to a window status of the vehicle.

Abstract: A method and/or system for managing a database that stores space-time context objects is provided. The system receives a query range in a multi-dimensional space. The system maps the query range into a set of fragments of a space-filling curve that fills the multi-dimensional space in all dimensions of the multi-dimensional space. The system uses each mapped fragment in the set of mapped fragments as a key to query the database for space-time context objects that are mapped to the space-filling curve. The system queries the database by identifying one or more context objects that intersect the mapped fragment at the space-filling curve.

Abstract: Embodiments of the present invention may be directed toward a method, a system, and a computer program product of adaptive calibration of sensors through cognitive learning. In an exemplary embodiment, the method, the system, and the computer program product include (1) in response to receiving a data from at least one calibration sensor and data from an itinerant sensor, comparing the data from the at least one calibration sensor and the data from the itinerant sensor, (2) in response to the comparing, determining, by one or more processors, the accuracy of the itinerant sensor, (3) generating, by the one or more processors, one or more calibration parameters based on the determining and based on a machine learning associated with preexisting sensor information, and (4) executing, by the one or more processors, the one or more calibration parameters.

Abstract: Systems and methods for training a neural network model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks. And at last the DNN, the supervised classification network and the reconstruction network are trained as a whole based on the obtained features, the training being guided by the classification tasks and the reconstruction tasks.

Abstract: A mechanism is provided for controlling the internal air-quality of a vehicle, including configuring a control policy that controls an internal air-quality of a vehicle and performing an action dictated by the control policy according to a window status of the vehicle.

Abstract: A mechanism is provided for controlling the internal air-quality of a vehicle, including determining a changing trend of the in-vehicle air-quality based on acquired in-vehicle sensor data and usage status of the vehicle and responsive to the determined changing trend of the in-vehicle air-quality, signaling a control system of the vehicle to control the usage status of the vehicle based on a control policy.

Abstract: A method to train a machine learning model for in-vehicle air quality control in a knowledge-based system, executed by one or more computer processors, includes collecting data related to in-vehicle air quality from a plurality of probe cars where the data is collected by various on-board systems in each probe car. The method includes correlating the data related to in-vehicle air quality from each probe car with air quality measurements from each probe car, where the correlation is used to update the machine learning model. The method includes determining a situation when an in-vehicle air quality measurement of the air quality measurements is above a pre-determined in-vehicle air quality level and determining instructions for actions by one or more of the one or more on-board systems in each of the probe cars to maintain an in-vehicle air quality level at or below the pre-determined in-vehicle air quality level.

Abstract: An interconnection unit includes a first connector configured to be coupled to an electronic device. There is a second connector configured to be coupled to a power station and to provide a path to the electronic device via the first connector. There is a low pass filter coupled between the first connector and the second connector and configured to allow the electronic device to receive power from the power station while maintaining data security of the electronic device.

Abstract: A method, system, and computer program product, include receiving a plurality of requests for dynamic context information from a plurality of road segments, determining whether the plurality of road segments are included in a same cluster of road segments in a road network generated by clustering road segments in the road network based on connectivity of the road network; and consolidating the plurality of requests to generate a consolidated request in response to determining that the plurality of road segments are included in the same cluster.

Abstract: A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.

Abstract: In one embodiment, a computer-implemented method includes receiving training data including a plurality of records, each record having a plurality of attributes. The training data is horizontally parallelized across two or more processing elements. This horizontal parallelizing includes dividing the training data into two or more subsets of records; assigning each subset of records to a corresponding processing element of the two or more processing elements; transmitting each subset of records to its assigned processing element; and sorting, at the two or more processing elements, the two or more subsets of records to two or more candidate leaves of a decision tree. The output from horizontally parallelizing is converted into input for vertically parallelizing the training data. The training data is vertically parallelized across the two or more processing elements. The decision tree is grown based at least in part on the horizontally parallelizing, the converting, and the vertically parallelizing.

Abstract: In one embodiment, a computer-implemented method includes receiving training data including a plurality of records, each record having a plurality of attributes. The training data is horizontally parallelized across two or more processing elements. This horizontal parallelizing includes dividing the training data into two or more subsets of records; assigning each subset of records to a corresponding processing element of the two or more processing elements; transmitting each subset of records to its assigned processing element; and sorting, at the two or more processing elements, the two or more subsets of records to two or more candidate leaves of a decision tree. The output from horizontally parallelizing is converted into input for vertically parallelizing the training data. The training data is vertically parallelized across the two or more processing elements. The decision tree is grown based at least in part on the horizontally parallelizing, the converting, and the vertically parallelizing.

Abstract: Methods and systems for reinforcement learning with dynamic agent grouping include gathering information at a first agent using one or more sensors. Shared information is received at the first agent from a second agent. An agent model is trained at the first agent using the gathered information and the shared information. A contribution of the shared information is weighted according to a degree of similarity between the first agent and the second agent. An action is generated using the trained agent model responsive to the gathered information.

Abstract: A method and system to perform spatio-temporal prediction are described. The method includes obtaining, based on communication with one or more sources, multi-scale spatial datasets, each of the multi-scale spatial datasets providing a type of information at a corresponding granularity, at least two of the multi-scale spatial datasets providing at least two types of information at different corresponding granularities. The method also includes generating new features for each of the multi-scale spatial datasets, the new features being based on features of each of the multi-scale spatial datasets and spatial relationships between and within the multi-scale spatial datasets. The method further includes selecting, using the processor, features of interest from among the new features, training a predictive model based on the features of interest, and predicting an event based on the predictive model.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: A method and an apparatus for determining a location of a mobile device. The location of a mobile device is determined accurately according to information which includes call data records of the mobile device. By employing a partial ellipse integral model, two physical world factors are taken into consideration in reducing the location uncertainty in call data records. The factors include: spatiotemporal constraints of the device's movement in the physical world and the telecommunication cell area's geometry information, which increase the accuracy of determining the location of a mobile device.

Abstract: A method and system to identify a time lagged indicator of an event to be predicted are described. The method includes receiving information including an indication of a factor, the factor being a different event than the event to be predicted, and identifying a window period within which the event is statistically correlated with the factor. The method also includes collecting data for a duration of the window period, the data indicating occurrences of the factor and the event, and identifying a time lagged dependency of the event on the factor based on analyzing the data.

Abstract: A mechanism is provided for controlling the internal air-quality of a vehicle. In-vehicle sensor data of a vehicle are acquired and the usage status of the vehicle is determined based on the acquired in-vehicle sensor data. Based on the acquired in-vehicle sensor data and the determined usage status, a changing trend of the in-vehicle air-quality is determined and responsive to the determined changing trend of the in-vehicle air-quality, a control system of the vehicle is signaled to control the usage status of the vehicle based on a control policy.